{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T21:16:02Z","timestamp":1762809362153,"version":"3.41.2"},"reference-count":45,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2023,2,3]],"date-time":"2023-02-03T00:00:00Z","timestamp":1675382400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,3,19]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>As the number of protein sequences increases in biological databases, computational methods are required to provide accurate functional annotation with high coverage. Although several machine learning methods have been proposed for this purpose, there are still two main issues: (i) construction of reliable positive and negative training and validation datasets, and (ii) fair evaluation of their performances based on predefined experimental settings. To address these issues, we have developed ProFAB: Open Protein Functional Annotation Benchmark, which is a platform providing an infrastructure for a fair comparison of protein function prediction methods. ProFAB provides filtered and preprocessed protein annotation datasets and enables the training and evaluation of function prediction methods via several options. We believe that ProFAB will be useful for both computational and experimental researchers by enabling the utilization of ready-to-use datasets and machine learning algorithms for protein function prediction based on Gene Ontology terms and Enzyme Commission numbers. ProFAB is available at https:\/\/github.com\/kansil\/ProFAB and https:\/\/profab.kansil.org.<\/jats:p>","DOI":"10.1093\/bib\/bbac627","type":"journal-article","created":{"date-parts":[[2023,2,4]],"date-time":"2023-02-04T00:03:09Z","timestamp":1675468989000},"source":"Crossref","is-referenced-by-count":1,"title":["ProFAB\u2014open protein functional annotation benchmark"],"prefix":"10.1093","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2096-9295","authenticated-orcid":false,"given":"A Samet","family":"\u00d6zdilek","sequence":"first","affiliation":[{"name":"Middle East Technical University Department of Health Informatics, Graduate School of Informatics, , Ankara, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ahmet","family":"Atakan","sequence":"additional","affiliation":[{"name":"Middle East Technical University Department of Computer Engineering, , Ankara, Turkey"},{"name":"Erzincan Binali Y\u0131ld\u0131r\u0131m University Department of Computer Engineering, , Erzincan, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"G\u00f6khan","family":"\u00d6zsar\u0131","sequence":"additional","affiliation":[{"name":"Middle East Technical University Department of Computer Engineering, , Ankara, Turkey"},{"name":"Ni\u011fde \u00d6mer Halisdemir University Department of Computer Engineering, , Ni\u011fde, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aybar","family":"Acar","sequence":"additional","affiliation":[{"name":"Middle East Technical University Department of Health Informatics, Graduate School of Informatics, , Ankara, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"M Volkan","family":"Atalay","sequence":"additional","affiliation":[{"name":"Middle East Technical University Department of Computer Engineering, , Ankara, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1298-9763","authenticated-orcid":false,"given":"Tunca","family":"Do\u011fan","sequence":"additional","affiliation":[{"name":"Hacettepe University Department of Computer Engineering and Artificial Intelligence Engineering, , Ankara, Turkey"},{"name":"Hacettepe University Department of Bioinformatics, Graduate School of Health Sciences, , Ankara, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ahmet S","family":"Rifaio\u011flu","sequence":"additional","affiliation":[{"name":"\u0130skenderun Technical University Department of Electrical-Electronics Engineering, , Hatay, Turkey"},{"name":"Heidelberg University and Heidelberg University Hospital Institute for Computational Biomedicine, Faculty of Medicine, , Heidelberg, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2023,2,3]]},"reference":[{"key":"2023032004263578200_","doi-asserted-by":"crossref","first-page":"2264","DOI":"10.1093\/bioinformatics\/btw114","article-title":"UniProt-DAAC: domain architecture alignment and classification, a new method for automatic functional annotation in UniProtKB","volume":"32","author":"Do\u011fan","year":"2016","journal-title":"Bioinformatics"},{"key":"2023032004263578200_","first-page":"1","article-title":"DEEPred: automated protein function prediction with multi-task feed-forward deep neural networks","volume":"9","author":"Rifaioglu","year":"2019","journal-title":"Sci Rep"},{"key":"2023032004263578200_","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1002\/prot.25416","article-title":"Large-scale automated function prediction of protein sequences and an experimental case study validation on PTEN transcript 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